{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
    "</div>\n",
    "\n",
    "To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CkLG6TLp-KUt"
   },
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b4SIxudc-KUt"
   },
   "source": [
    "\n",
    "Introducing FP8 precision training for faster RL inference. [Read Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning).\n",
    "\n",
    "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n",
    "\n",
    "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n",
    "\n",
    "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "edPZXqH6-KUu"
   },
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "05Y0VKOH-KUu",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": "%%capture\nimport os\n\n!pip install pip3-autoremove\n!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu128\n!pip install unsloth\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2\n!pip install decord"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9xLDGk41C7IF"
   },
   "source": [
    "### Unsloth"
   ]
  },
  {
   "cell_type": "code",
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    },
    "id": "QmUBVEnvCDJv",
    "outputId": "2af28e1e-aa2c-428c-ba8f-73857988dee0",
    "vscode": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/unsloth/ERNIE-4.5-VL-28B-A3B-PT:\n",
      "- configuration_ernie4_5_vl.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: WARNING `trust_remote_code` is True.\n",
      "Are you certain you want to do remote code execution?\n",
      "==((====))==  Unsloth 2025.11.3: Fast Dfnrope_Vision_Transformer patching. Transformers: 4.56.2.\n",
      "   \\\\   /|    NVIDIA A100-SXM4-80GB. Num GPUs = 1. Max memory: 79.318 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.9.0+cu126. CUDA: 8.0. CUDA Toolkit: 12.6. Triton: 3.5.0\n",
      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.33.post1. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
      "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n"
     ]
    },
    {
     "data": {
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       "model_id": "a2fbe834187047828c0b3285fa06c28e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "modeling_ernie4_5_vl.py: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/unsloth/ERNIE-4.5-VL-28B-A3B-PT:\n",
      "- modeling_ernie4_5_vl.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
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      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
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      ]
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    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The repository unsloth/ERNIE-4.5-VL-28B-A3B-PT contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/unsloth/ERNIE-4.5-VL-28B-A3B-PT .\n",
      " You can inspect the repository content at https://hf.co/unsloth/ERNIE-4.5-VL-28B-A3B-PT.\n",
      "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
      "\n",
      "Do you wish to run the custom code? [y/N] y\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/unsloth/ERNIE-4.5-VL-28B-A3B-PT:\n",
      "- processing_ernie4_5_vl.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "tokenizer.model:   0%|          | 0.00/1.61M [00:00<?, ?B/s]"
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     },
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    },
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
    "import torch\n",
    "from transformers import AutoModelForCausalLM ,AutoProcessor\n",
    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
    "fourbit_models = [\n",
    "    \"unsloth/Qwen3-VL-8B-Instruct-bnb-4bit\", # Qwen 3 vision support\n",
    "    \"unsloth/Qwen3-VL-8B-Thinking-bnb-4bit\",\n",
    "    \"unsloth/Qwen3-VL-32B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen3-VL-32B-Thinking-bnb-4bit\",\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model_path = \"unsloth/ERNIE-4.5-VL-28B-A3B-PT\"\n",
    "model, tokenizer = FastVisionModel.from_pretrained(\n",
    "    model_path,\n",
    "    auto_model=AutoModelForCausalLM,\n",
    "    load_in_4bit = False, # Unsupported for this specific model variant\n",
    "    trust_remote_code = True,\n",
    "    unsloth_force_compile = True,\n",
    "    use_gradient_checkpointing = False,\n",
    "    attn_implementation=\"eager\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S5xLHboFaBd7"
   },
   "source": [
    "We now load the processor"
   ]
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    "outputId": "8a801223-5999-4f32-d0aa-d505adc652b2",
    "vscode": {
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   "outputs": [
    {
     "data": {
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       "model_id": "3b341b4b3193441cb274e648cd64a71a",
       "version_major": 2,
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      "text/plain": [
       "processor_config.json:   0%|          | 0.00/308 [00:00<?, ?B/s]"
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    },
    {
     "data": {
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   "source": [
    "processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)\n",
    "processor.eval()\n",
    "model.add_image_preprocess(processor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters for parameter efficient finetuning - this allows us to only efficiently train 1% of all parameters.\n",
    "\n",
    "**[NEW]** We also support finetuning ONLY the vision part of the model, or ONLY the language part. Or you can select both! You can also select to finetune the attention or the MLP layers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "23cc59fa-2c59-4f72-df99-9a02e4f5192e",
    "vscode": {
     "languageId": "python"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Making `model.base_model.model.model` require gradients\n"
     ]
    }
   ],
   "source": [
    "model = FastVisionModel.get_peft_model(\n",
    "    model,\n",
    "    r = 8,\n",
    "    lora_alpha = 16,\n",
    "    lora_dropout = 0,\n",
    "    bias = \"none\",\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,\n",
    "    target_modules = [\n",
    "        \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "        \"gate_proj\", \"up_proj\", \"down_proj\",\n",
    "        \"fc1\", \"fc2\",\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vITh0KVJ10qX"
   },
   "source": [
    "<a name=\"Data\"></a>\n",
    "### Data Prep\n",
    "We'll be using a sampled dataset of handwritten maths formulas. The goal is to convert these images into a computer readable form - ie in LaTeX form, so we can render it. This can be very useful for complex formulas.\n",
    "\n",
    "You can access the dataset [here](https://huggingface.co/datasets/unsloth/LaTeX_OCR). The full dataset is [here](https://huggingface.co/datasets/linxy/LaTeX_OCR)."
   ]
  },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3f7546824054cefbec1477d735e4e62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/519 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb487c91046e4f65bdae7d1972fddc44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/train-00000-of-00001.parquet:   0%|          | 0.00/344M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e41eace282ce48fa9a67fe714b218717",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/test-00000-of-00001.parquet:   0%|          | 0.00/38.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72dc8dd898f14d3b8872d00b94cd69da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/68686 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39cbb95d51da4d64a4868076fbac34d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/7632 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"unsloth/LaTeX_OCR\", split = \"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W1W2Qhsz6rUT"
   },
   "source": [
    "Let's take an overview look at the dataset. We shall see what the 3rd image is, and what caption it had."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bfcSGwIb6p_R",
    "outputId": "78d3e3ff-903d-411b-e3c9-623fb997a0ff",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['image', 'text'],\n",
       "    num_rows: 68686\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "id": "uOLWY2936t1n",
    "outputId": "d004b5ed-2ce5-4b1b-ed49-2d37564c83d4",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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FFAH/2Q==",
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=320x50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[2][\"image\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "id": "VTzhtzNRAEL1",
    "outputId": "2fc25ea8-3f82-45a2-ae03-e47d8fc392ce",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'H ^ { \\\\prime } = \\\\beta N \\\\int d \\\\lambda \\\\biggl \\\\{ \\\\frac { 1 } { 2 \\\\beta ^ { 2 } N ^ { 2 } } \\\\partial _ { \\\\lambda } \\\\zeta ^ { \\\\dagger } \\\\partial _ { \\\\lambda } \\\\zeta + V ( \\\\lambda ) \\\\zeta ^ { \\\\dagger } \\\\zeta \\\\biggr \\\\} \\\\ .'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[2][\"text\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NAeQ9LXCAEkW"
   },
   "source": [
    "We can also render the LaTeX in the browser directly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 58
    },
    "id": "lXjfJr4W6z8P",
    "outputId": "6975090d-a45b-45b1-ab16-108bd66aad3f",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle H ^ { \\prime } = \\beta N \\int d \\lambda \\biggl \\{ \\frac { 1 } { 2 \\beta ^ { 2 } N ^ { 2 } } \\partial _ { \\lambda } \\zeta ^ { \\dagger } \\partial _ { \\lambda } \\zeta + V ( \\lambda ) \\zeta ^ { \\dagger } \\zeta \\biggr \\} \\ .$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Math, Latex\n",
    "\n",
    "latex = dataset[2][\"text\"]\n",
    "display(Math(latex))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9CBpiISFa6C"
   },
   "source": [
    "To format the dataset, all vision finetuning tasks should be formatted as follows:\n",
    "\n",
    "```python\n",
    "[\n",
    "{ \"role\": \"user\",\n",
    "  \"content\": [{\"type\": \"text\",  \"text\": Q}, {\"type\": \"image\", \"image\": image} ]\n",
    "},\n",
    "{ \"role\": \"assistant\",\n",
    "  \"content\": [{\"type\": \"text\",  \"text\": A} ]\n",
    "},\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oPXzJZzHEgXe",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "instruction = \"Write the LaTeX representation for this image.\"\n",
    "\n",
    "def convert_to_conversation(sample):\n",
    "    conversation = [\n",
    "        { \"role\": \"user\",\n",
    "          \"content\" : [\n",
    "            {\"type\" : \"text\",  \"text\"  : instruction},\n",
    "            {\"type\" : \"image\", \"image\" : sample[\"image\"]} ]\n",
    "        },\n",
    "        { \"role\" : \"assistant\",\n",
    "          \"content\" : [\n",
    "            {\"type\" : \"text\",  \"text\"  : sample[\"text\"]} ],\n",
    "            \"reasoning_content\": \"\\n\" # If you leave this as \"\\n\", you train the model to output empty thoughts.\n",
    "        },\n",
    "    ]\n",
    "    return { \"messages\" : conversation }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FY-9u-OD6_gE"
   },
   "source": [
    "Let's convert the dataset into the \"correct\" format for finetuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gFW2qXIr7Ezy",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "converted_dataset = [convert_to_conversation(sample) for sample in dataset]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ndDUB23CGAC5"
   },
   "source": [
    "We look at how the conversations are structured for the first example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gGFzmplrEy9I",
    "outputId": "c3013ad5-a0d6-4a41-ea16-0fe305dbeb47",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [{'role': 'user',\n",
       "   'content': [{'type': 'text',\n",
       "     'text': 'Write the LaTeX representation for this image.'},\n",
       "    {'type': 'image',\n",
       "     'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=160x40>}]},\n",
       "  {'role': 'assistant',\n",
       "   'content': [{'type': 'text',\n",
       "     'text': '{ \\\\frac { N } { M } } \\\\in { \\\\bf Z } , { \\\\frac { M } { P } } \\\\in { \\\\bf Z } , { \\\\frac { P } { Q } } \\\\in { \\\\bf Z }'}],\n",
       "   'reasoning_content': '\\n'}]}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converted_dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FecKS-dA82f5"
   },
   "source": [
    "Let's first see before we do any finetuning what the model outputs for the first example!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f8Wv2Q1PhS8r",
    "outputId": "6a21b1f5-9fe3-4264-aec9-dd7041cbb91c",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The LaTeX code for the Hamiltonian in the image is:\n",
      "H'=\\beta N\\int d\\lambda\\left\\{\\frac{1}{2\\beta^{2}N^{2}}\\partial_{\\xi}\\lambda^{\\dagger}\\partial_{\\xi}\\lambda+V(\\lambda)\\zeta^{\\dagger}\\zeta\\right\\}~.</s>\n"
     ]
    }
   ],
   "source": [
    "FastVisionModel.for_inference(model) # Enable for inference!\n",
    "\n",
    "image = dataset[2][\"image\"]\n",
    "\n",
    "instruction = \"Write the LaTeX representation for this image.\"\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"image\"}, # Placeholder required for the template\n",
    "            {\"type\": \"text\", \"text\": instruction}\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "text_prompt = processor.tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    tokenize=False,\n",
    "    add_generation_prompt=True,\n",
    "    enable_thinking=False\n",
    ")\n",
    "inputs = processor(\n",
    "    text=[text_prompt],\n",
    "    images=[image],\n",
    "    videos=[],\n",
    "    padding=True,\n",
    "    return_tensors=\"pt\",\n",
    ")\n",
    "\n",
    "# Move inputs to GPU\n",
    "device = next(model.parameters()).device\n",
    "inputs = inputs.to(device)\n",
    "\n",
    "from transformers import TextStreamer\n",
    "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens=128,\n",
    "                   use_cache=False, temperature=1.5, min_p=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "YzI6eq34LoEZ",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "# @title Setup Collator & Trainer\n",
    "\n",
    "from trl import SFTTrainer, SFTConfig\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from dataclasses import dataclass, field\n",
    "from typing import Any, Dict, List, Optional, Union\n",
    "\n",
    "@dataclass\n",
    "class ErnieVisionDataCollator:\n",
    "    processor: Any\n",
    "    tokenizer: Any\n",
    "    ignore_index: int = -100\n",
    "    max_seq_length: int = 2048\n",
    "    train_on_responses_only: bool = False\n",
    "\n",
    "    _img_patch_id: int = field(init=False, default=-1)\n",
    "\n",
    "    def __post_init__(self):\n",
    "        if self.tokenizer.pad_token_id is None:\n",
    "            self.tokenizer.pad_token_id = 0\n",
    "        patch_token = \"<|IMAGE_PLACEHOLDER|>\"\n",
    "        converted_id = self.tokenizer.convert_tokens_to_ids(patch_token)\n",
    "        self._img_patch_id = converted_id if converted_id is not None else -1\n",
    "\n",
    "    def _extract_visuals(self, msgs: List[Dict]) -> tuple:\n",
    "        image_inputs, video_inputs = [], []\n",
    "        needs_extraction = False\n",
    "\n",
    "        for msg in msgs:\n",
    "            content = msg.get(\"content\", [])\n",
    "            if isinstance(content, list):\n",
    "                for part in content:\n",
    "                    if isinstance(part, dict):\n",
    "                        if \"image\" in part:\n",
    "                            image_inputs.append(part[\"image\"])\n",
    "                        elif part.get(\"type\") in [\"image_url\", \"video_url\"]:\n",
    "                            needs_extraction = True\n",
    "\n",
    "        if needs_extraction and not image_inputs:\n",
    "            try:\n",
    "                return self.processor.process_vision_info(msgs)\n",
    "            except Exception:\n",
    "                return [], []\n",
    "\n",
    "        return image_inputs, video_inputs\n",
    "\n",
    "    def _mask_prompt(self, msgs: List[Dict], image_inputs: List, labels: torch.Tensor, full_input_ids: torch.Tensor) -> torch.Tensor:\n",
    "        last_asst_idx = -1\n",
    "        for i in range(len(msgs) - 1, -1, -1):\n",
    "            if msgs[i]['role'] == 'assistant':\n",
    "                last_asst_idx = i\n",
    "                break\n",
    "\n",
    "        if last_asst_idx == -1:\n",
    "            return labels\n",
    "\n",
    "        prompt_msgs = msgs[:last_asst_idx]\n",
    "        prompt_text = self.tokenizer.apply_chat_template(\n",
    "            prompt_msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False\n",
    "        )\n",
    "\n",
    "        prompt_inputs = self.processor(\n",
    "            text=[prompt_text],\n",
    "            images=image_inputs,\n",
    "            return_tensors=\"pt\"\n",
    "        )\n",
    "\n",
    "        prompt_ids = prompt_inputs['input_ids'][0]\n",
    "\n",
    "        len_full = full_input_ids.size(0)\n",
    "        len_prompt = prompt_ids.size(0)\n",
    "        limit = min(len_full, len_prompt)\n",
    "\n",
    "        matches = (full_input_ids[:limit] == prompt_ids[:limit])\n",
    "\n",
    "        mismatches = (~matches).nonzero(as_tuple=False)\n",
    "\n",
    "        if len(mismatches) > 0:\n",
    "            mask_len = mismatches[0].item()\n",
    "        else:\n",
    "            mask_len = limit\n",
    "\n",
    "        labels[:mask_len] = self.ignore_index\n",
    "\n",
    "        return labels\n",
    "\n",
    "    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:\n",
    "        batch = {k: [] for k in [\"input_ids\", \"labels\", \"token_type_ids\", \"position_ids\", \"images\", \"grid_thw\", \"image_type_ids\"]}\n",
    "\n",
    "        for example in features:\n",
    "            msgs = example.get(\"messages\", example.get(\"conversations\", []))\n",
    "            image_inputs, video_inputs = self._extract_visuals(msgs)\n",
    "\n",
    "            text = self.tokenizer.apply_chat_template(\n",
    "                msgs, tokenize=False, add_generation_prompt=False\n",
    "            )\n",
    "\n",
    "            inputs = self.processor(\n",
    "                text=[text],\n",
    "                images=image_inputs,\n",
    "                videos=video_inputs,\n",
    "                return_tensors=\"pt\"\n",
    "            )\n",
    "\n",
    "            input_ids = inputs['input_ids'][0]\n",
    "            tt = inputs['token_type_ids'][0]\n",
    "            pos = inputs['position_ids'][0]\n",
    "\n",
    "            if input_ids[-1] != self.tokenizer.eos_token_id:\n",
    "                input_ids = torch.cat([input_ids, torch.tensor([self.tokenizer.eos_token_id])])\n",
    "                tt = torch.cat([tt, torch.tensor([0], dtype=tt.dtype)])\n",
    "                pos = torch.cat([pos, (pos[-1] + 1).unsqueeze(0)])\n",
    "\n",
    "            labels = input_ids.clone()\n",
    "\n",
    "\n",
    "            if self._img_patch_id != -1:\n",
    "                labels[labels == self._img_patch_id] = self.ignore_index\n",
    "\n",
    "            if self.train_on_responses_only:\n",
    "                labels = self._mask_prompt(msgs, image_inputs, labels, input_ids)\n",
    "\n",
    "            batch[\"input_ids\"].append(input_ids)\n",
    "            batch[\"labels\"].append(labels)\n",
    "            batch[\"token_type_ids\"].append(torch.cat([tt, torch.tensor([0])]))\n",
    "            batch[\"position_ids\"].append(pos)\n",
    "\n",
    "            if inputs.get('images') is not None: batch[\"images\"].append(inputs['images'])\n",
    "            if inputs.get('grid_thw') is not None: batch[\"grid_thw\"].append(inputs['grid_thw'])\n",
    "            if inputs.get('image_type_ids') is not None: batch[\"image_type_ids\"].append(inputs['image_type_ids'])\n",
    "\n",
    "        padded_input = torch.nn.utils.rnn.pad_sequence(batch[\"input_ids\"], batch_first=True, padding_value=self.tokenizer.pad_token_id)\n",
    "        padded_label = torch.nn.utils.rnn.pad_sequence(batch[\"labels\"], batch_first=True, padding_value=self.ignore_index)\n",
    "        padded_tt = torch.nn.utils.rnn.pad_sequence(batch[\"token_type_ids\"], batch_first=True, padding_value=0)\n",
    "\n",
    "        max_len = padded_input.shape[1]\n",
    "        padded_pos = torch.zeros((len(batch[\"position_ids\"]), max_len, 3), dtype=torch.long)\n",
    "        for i, p in enumerate(batch[\"position_ids\"]):\n",
    "            l = min(p.shape[0], max_len)\n",
    "            padded_pos[i, :l, :] = p[:l]\n",
    "\n",
    "        if padded_input.shape[1] > self.max_seq_length:\n",
    "            padded_input = padded_input[:, :self.max_seq_length]\n",
    "            padded_label = padded_label[:, :self.max_seq_length]\n",
    "            padded_pos = padded_pos[:, :self.max_seq_length, :]\n",
    "            padded_tt = padded_tt[:, :self.max_seq_length + 1]\n",
    "\n",
    "        final_batch = {\n",
    "            \"input_ids\": padded_input,\n",
    "            \"labels\": padded_label,\n",
    "            \"attention_mask\": padded_input.ne(self.tokenizer.pad_token_id).long(),\n",
    "            \"token_type_ids\": padded_tt,\n",
    "            \"position_ids\": padded_pos,\n",
    "        }\n",
    "\n",
    "        if batch[\"images\"]: final_batch[\"images\"] = torch.cat(batch[\"images\"], dim=0)\n",
    "        if batch[\"grid_thw\"]: final_batch[\"grid_thw\"] = torch.cat(batch[\"grid_thw\"], dim=0)\n",
    "        if batch[\"image_type_ids\"]: final_batch[\"image_type_ids\"] = torch.cat(batch[\"image_type_ids\"], dim=0)\n",
    "\n",
    "        return final_batch\n",
    "\n",
    "class ErnieSFTTrainer(SFTTrainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n",
    "        outputs = model(**inputs)\n",
    "\n",
    "        logits = outputs.logits\n",
    "        labels = inputs.get(\"labels\")\n",
    "\n",
    "        loss = None\n",
    "        if labels is not None:\n",
    "\n",
    "            shift_logits = logits[..., :-1, :].contiguous()\n",
    "            shift_labels = labels[..., 1:].contiguous()\n",
    "\n",
    "            loss_fct = nn.CrossEntropyLoss()\n",
    "            shift_logits = shift_logits.view(-1, self.model.config.vocab_size)\n",
    "            shift_labels = shift_labels.view(-1)\n",
    "\n",
    "            loss = loss_fct(shift_logits, shift_labels)\n",
    "\n",
    "            if hasattr(outputs, \"router_loss\") and outputs.router_loss is not None:\n",
    "                aux_loss = outputs.router_loss.to(loss.device)\n",
    "                loss += aux_loss\n",
    "\n",
    "        if return_outputs:\n",
    "            return (loss, outputs)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's train our model. We do 30 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!\n",
    "\n",
    "We use our new `ErnieVisionDataCollator` which will help in our vision finetuning setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "95_Nn-89DhsL",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "from trl import  SFTConfig\n",
    "\n",
    "FastVisionModel.for_training(model) # Enable for training!\n",
    "\n",
    "custom_collator = ErnieVisionDataCollator(\n",
    "    processor=processor,\n",
    "    tokenizer=tokenizer,\n",
    "    max_seq_length=2048,\n",
    "    train_on_responses_only = True,\n",
    ")\n",
    "\n",
    "trainer = ErnieSFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = processor.tokenizer,\n",
    "    data_collator = custom_collator,\n",
    "    train_dataset = converted_dataset,\n",
    "    args = SFTConfig(\n",
    "        per_device_train_batch_size = 2,\n",
    "        gradient_accumulation_steps = 2,\n",
    "        warmup_steps = 5,\n",
    "        max_steps = 30,\n",
    "        learning_rate = 2e-4,\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.001,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        output_dir = \"outputs\",\n",
    "        report_to = \"none\",\n",
    "        gradient_checkpointing = False,\n",
    "        remove_unused_columns = False,\n",
    "        dataset_text_field = \"\",\n",
    "        dataset_kwargs = {\"skip_prepare_dataset\": True},\n",
    "        max_length = 2048,\n",
    "        fp16 = not torch.cuda.is_bf16_supported(),\n",
    "        bf16 = torch.cuda.is_bf16_supported(),\n",
    "    ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "3634bdfe-c9bc-4104-d1b8-487c470e5c43",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = NVIDIA A100-SXM4-80GB. Max memory = 79.318 GB.\n",
      "56.051 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "# @title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "4157313e-4646-438c-a29b-448c0689c209",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
      "   \\\\   /|    Num examples = 68,686 | Num Epochs = 1 | Total steps = 30\n",
      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient accumulation steps = 2\n",
      "\\        /    Data Parallel GPUs = 1 | Total batch size (2 x 2 x 1) = 4\n",
      " \"-____-\"     Trainable parameters = 308,224,000 of 29,707,518,336 (1.04% trained)\n",
      "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [30/30 11:58, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.039000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.360200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.705200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.301600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.836600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.906900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.610100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.609500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.671800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.193100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.242900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.288600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.719300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.297700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.222700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.300200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.207000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.247700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.207400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.263100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.111000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.279800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.466100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.232500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.228000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.406800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.109900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.165400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.366600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.411600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pCqnaKmlO1U9",
    "outputId": "cf68f8c3-b34e-4897-f4e1-5b312875a4bb",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "755.0562 seconds used for training.\n",
      "12.58 minutes used for training.\n",
      "Peak reserved memory = 62.695 GB.\n",
      "Peak reserved memory for training = 6.644 GB.\n",
      "Peak reserved memory % of max memory = 79.043 %.\n",
      "Peak reserved memory for training % of max memory = 8.376 %.\n"
     ]
    }
   ],
   "source": [
    "# @title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory / max_memory * 100, 3)\n",
    "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(\n",
    "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
    ")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model! You can change the instruction and input - leave the output blank!\n",
    "\n",
    "We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "b50d1f07-8d3a-4471-f41e-cfa2f62db834",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H ^ { \\prime } = \\beta N \\int d \\lambda \\left\\{ \\frac { 1 } { 2 \\beta ^ { 2 } N ^ { 2 } } \\partial _ { \\lambda } \\zeta ^ { \\dagger } \\partial _ { \\lambda } \\zeta + V ( \\lambda ) \\zeta ^ { \\dagger } \\zeta \\right\\} \\ .<|end_of_sentence|>\n"
     ]
    }
   ],
   "source": [
    "FastVisionModel.for_inference(model) # Enable for inference!\n",
    "\n",
    "image = dataset[2][\"image\"]\n",
    "\n",
    "instruction = \"Write the LaTeX representation for this image.\"\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"image\"}, # Placeholder required for the template\n",
    "            {\"type\": \"text\", \"text\": instruction}\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "text_prompt = processor.tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    tokenize=False,\n",
    "    add_generation_prompt=True,\n",
    "    enable_thinking=False\n",
    ")\n",
    "inputs = processor(\n",
    "    text=[text_prompt],\n",
    "    images=[image],\n",
    "    videos=[],\n",
    "    padding=True,\n",
    "    return_tensors=\"pt\",\n",
    ")\n",
    "\n",
    "# Move inputs to GPU\n",
    "device = next(model.parameters()).device\n",
    "inputs = inputs.to(device)\n",
    "\n",
    "from transformers import TextStreamer\n",
    "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens=128,\n",
    "                   use_cache=False, temperature=1.5, min_p=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "upcOlWe7A1vc",
    "outputId": "50f0a2f9-755a-4dbe-c717-8193e00e2730",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('lora_model/tokenizer_config.json',\n",
       " 'lora_model/special_tokens_map.json',\n",
       " 'lora_model/chat_template.jinja',\n",
       " 'lora_model/tokenizer.model',\n",
       " 'lora_model/added_tokens.json')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_pretrained(\"lora_model\")  # Local saving\n",
    "tokenizer.save_pretrained(\"lora_model\")\n",
    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
    "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEEcJ4qfC7Lp"
   },
   "source": [
    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MKX_XKs_BNZR",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    from unsloth import FastVisionModel\n",
    "    model, tokenizer = FastVisionModel.from_pretrained(\n",
    "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        load_in_4bit = False, # Set to False for 16bit LoRA\n",
    "    )\n",
    "    FastVisionModel.for_inference(model) # Enable for inference!\n",
    "\n",
    "\n",
    "from transformers import TextStreamer\n",
    "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens=128,\n",
    "                   use_cache=False, temperature=1.5, min_p=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16 for VLLM\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iHjt_SMYsd3P",
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "# Select ONLY 1 to save! (Both not needed!)\n",
    "\n",
    "# Save locally to 16bit\n",
    "if False: model.save_pretrained_merged(\"unsloth_finetune\", tokenizer,)\n",
    "\n",
    "# To export and save to your Hugging Face account\n",
    "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", tokenizer, token = \"PUT_HERE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
    "\n",
    "  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)\n",
    "</div>\n"
   ]
  }
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